Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
In the response filed on 02 April 2026, no claims have been amended, added or canceled.
Now claims 1-10, 12-18 and 20-22 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10, 12-18 and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 16 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite computer implemented method, system and non-transitory computer-readable storage medium (CRM) for performing the limitations of:
Claim 1, which is representative of claims 16 and 20
[…] improving accuracy of food weight measurement […], the method comprising: [… collect …] a plurality of food intake events of a user occurred at a plurality of venues, wherein the obtaining comprises: collecting first food intake events of the […], wherein the collecting comprises: continuously collecting weight readings […] to measure weight changes of a food container at millisecond intervals; obtaining a plurality of 3D point cloud images with depth information capturing hand motions of the user; feeding, in real time, the plurality of 3D point cloud images into a [… model …] for identifying a user action based on the hand motions, wherein the user action comprises lifting a serving utensil, taking food using the serving utensil from the food container, dropping food back to the food container, stirring food, or placing the serving utensil back to the food container; determining, in real time, […], the user action associated with a time window of weight fluctuation in the weight readings, wherein the time window is between a start and a completion of the user action; determining, in real time, based on the determined user action, whether the weight fluctuation during the time window are attributable to food removal or to a non-serving event including at least one of stirring food, fluid sloshing, utensil movement, or environmental vibration that causes transient weight fluctuation; adjusting, in real time, the weight readings […] to compensate the weight fluctuation based on the determined user action, wherein the adjusting automatically compensates for the transient weight fluctuations caused by the non-serving event, thereby improving accuracy of weight measurements in the presence of transient physical disturbances; determining, in real time, an amount of food taken by the user based on the adjusted weight readings; determining, in real time, portion-based dietary information at least based on the amount of food taken by the user; and associating the portion-based dietary information of the first food intake event with an identification of the user to form a first food intake event; [… saving …] the plurality of food intake events based on the identification of the user associated with the plurality of food intake events; and automatically generating a dietary analysis report for the user based on the plurality of food intake events of the user collected from the plurality of venues; and [… providing …] the dietary analysis report to […] the user such that the user has access to an end-to-end coverage of dietary features of the user, the method further comprising [… creating a model …] based on visual data collected […] for user action recognition, wherein the visual data comprises hand motions or trajectories of a given user with depth information and labeled user action, and the [… creating …] comprises: computing a distance between predicted user action […] and the labeled user action; and [… updating the model …] to minimize the distance in subsequent training.
, as drafted, is a method, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). That is by a human user interacting with a computer with an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claims 16 and 20), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by a human interacting with the a computer with an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, weight sensors, a 3D camera, a device (claims 16 and 20), the claim encompasses a user logging and monitoring a plurality of a dietary intake events, organizing the collected data with a model to determine features and determine user consumption to generate and provide a user a dietary report for a human user to use. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a computer with an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20), which implements the abstract idea. The an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20) an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20) are recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Figure 7, paragraphs [0129]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of weight sensors, a 3D camera, “obtaining… transmitting…”, “using the channel-separated neural network”, “storing…” and “training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” to implement the abstract idea. The weight sensors are recited at a high level of generality (i.e., a generic off the shelf sensor/scale attached to a generic container) and amounts to generally linking the abstract idea to a particular technological environment. The 3D camera is recited at a high level of generality (i.e., a generic off the shelf 3d camera) and amounts to generally linking the abstract idea to a particular technological environment. The “obtaining… transmitting…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “using the channel-separated neural network” steps are recited at a high-level of generality (i.e., using a generic off-the shelf model) and amounts to generally linking the abstract idea to a particular technological environment. The “storing…” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. The “training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” are recited at a high-level of generality (i.e., training and using a generic off-the shelf neural network that is updated) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computer with an Internet of things (IoT) system, a device (claim 1), one or more processors with one or more non-transitory CRMs, an Internet of things (IoT) system, a device (claims 16 and 20) to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of weight sensors, a 3D camera, “obtaining… transmitting…”, “using the channel-separated neural network”, “storing…” and ““training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” were considered generally linking the abstract idea to particular technological environment and/or extra-solution activity. The weight sensors have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): Figure 1, paragraphs [0047]-[0048]; Kim (2020/0365250): Figure 1, paragraphs [0007], [0014], [0094]; use of sensors to capture weight of a food container are well-understood, routine and conventional. The 3D camera has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): Figure 1, paragraphs [0047]-[0048]; Kim (2020/0365250): Figure 1, paragraphs [0014], [0094]; use of a camera to capture image of food is well-understood, routine and conventional. The “obtaining… transmitting…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “using the channel-separated neural network” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): paragraphs [0014]; Kim (2020/0365250): paragraph [0113]; use of an off the shelf-machine learning model is well-understood, routine and conventional. The “storing…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. The “training the channel-separated neural network… the training comprises… adjusting weights of the channel-separated neural network” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Guo (20200356842): paragraphs [0006], [0027], [0049]-[0052]; Armstrong (20200193620): Figures 5-6, paragraphs [0017], [0040], [0043]-[0045]; Denli (20190064389): Figure 2, paragraphs [0019], [0056], [0058]-[0062]; Chen (20210067527): Figure 2, paragraphs [0065]-[0066], [0091]-[0094]; Jha (20220374292): Figures 28, 30-31, paragraph [0035], [0089] and [0104]; use of a channel-separated neural network that is trained by adjusting weights is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-10, 12-15, 17-18 and 20-22 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2-3, 12, 15 and 17-18 recite the additional element of “displaying, on a display”, however this is recited at a high-level of generality (i.e., as a generic presentation of information to a user) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “displaying, on a display” was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): paragraphs [0064]; Kim (2020/0365250): paragraph [0020]; Ou (2018/0114601): paragraph [0253]; displaying information on a display is Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim 4 further describes updating of information, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 5 and 6 further describe association and creation of lists, however does not recite any additional elements not already considered above are therefore cannot provide a practical application and/or significantly more.
Claims 7 and 8 recite the additional elements of “clustering, using unsupervised learning” and “training, using supervised training”, however these are recited at a high level of generality (i.e., generic off-the shelf machine learning techniques) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “clustering, using unsupervised learning” and “training, using supervised training” were considered generally linking the abstract idea to particular technological environment. The “clustering, using unsupervised learning” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Malecha (2023/0060235): paragraph [0037]; Lee (20180157936): paragraph [0100]; Simpson (20210335499): paragraph [0135]-[0136]; use of unsupervised learning to cluster data is Well-understood, routine, and conventional elements. The “training, using supervised training” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Malecha (2023/0060235): paragraph [0165]; Lee (20180157936): paragraph [0100]; Simpson (20210335499): paragraph [0135]-[0136]; use of supervised learning to classify data is Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 9 and 10 further describe goal and feature determination, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 12 and 14 describe a machine learning model, however use of machine learning models was already considered above and is incorporated herein.
Claim 13 further describes detecting a behavior group for the report, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 21 and 22 recite the additional element of electronic appliance comprising a scale coupled with one or more weight sensors and a first camera, however this is recited at a high level of generality (i.e., a generic off the shelf camera and scale) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements were considered generally linking the abstract idea to particular technological environment. This been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Mossier (2022/0020471): Figure 1, paragraphs [0047]-[0048]; Kim (2020/0365250): Figure 1, paragraphs [0014], [0094]; use of an appliance with a scale and camera to capture image of food and measure the weight is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Response to Arguments
Applicant's arguments filed on 02 April 2026 have been fully considered but they are not persuasive. Applicant's arguments will be addressed below in the order in which they appear in the response filed on 02 April 2026.
Rejection under 35 U.S.C. § 101
Regarding the rejection of claim 1-10, 12-18 and 20-22, the Examiner has considered the Applicant's arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
As amended, independent claim 1 is directed to a specific, technology-focused method for improving, in real-time, the accuracy of food weight measurement in an IoT food service system by fusing high-frequency load-cell signals with 3D hand-motion data processed by a channel-separated neural network, in order to distinguish actual serving events from transient non-serving disturbances (sloshing, stirring, utensil movement, vibration) and automatically correct the sensor output… Claim 1 Solves a Technological Problem and is Not Directed to Organizing Human Activity… Paragraph [0060] of the specification explicitly identifies a technological problem inherent to digital weighing hardware: load cell sensors are highly sensitive and prone to generating inaccurate readings when subjected to "fluid sloshing," "material agitation or mixing (e.g., stirring)," or "vibration caused by surrounding environment." The fact that a human hand may be the initial physical catalyst for the vibration does not change the fact that the resulting noise on a digital load cell is a hardware signal processing problem… It utilizes a secondary sensor modality (a 3D depth camera) and a specific processing architecture (a channel-separated neural network) to identify the exact temporal boundaries of a physical disturbance, dynamically adjusting the load cell hardware readings in real time to filter out the noise. This is a technical improvement to a physical system, executing operations (processing 3D point clouds and adjusting millisecond weight fluctuations) that cannot practically be performed in the human mind… It utilizes a secondary sensor modality (a 3D depth camera) and a specific processing architecture (a channel-separated neural network) to identify the exact temporal boundaries of a physical disturbance, dynamically adjusting the load cell hardware readings in real time to filter out the noise. This is a technical improvement to a physical system, executing operations (processing 3D point clouds and adjusting millisecond weight fluctuations) that cannot practically be performed in the human mind… The Neural Network Training Limitations Are Integrated into the Practical Application… The Neural Network Training Limitations Are Integrated into the Practical Application… It is part of the technical solution: the network is explicitly trained so it can reliably distinguish serving actions from non-serving actions, and the output is then used to automatically adjust the weight readings. Based on Example 39, this limitation should be treated as an additional element, not as part of an alleged abstract idea… Applicant does not dispute that load cells, cameras, and neural networks per se were known. However, the correct analysis under Step 2B is whether the ordered combination of elements, as recited in the claim, is well-understood, routine, and conventional… This highly specific coupling of a trained channel-separated neural network with continuous load cell data to identify and subtract non-serving transient disturbances is the inventive concept in the claimed method… This highly specific coupling of a trained channel-separated neural network with continuous load cell data to identify and subtract non-serving transient disturbances is the inventive concept in the claimed method.
The Examiner respectfully disagrees.
It is respectfully submitted, that paragraph [0060] alone is not sufficient to explicitly recite a technical problem that one of ordinary skill in the art would understand is being solved by the claimed additional elements, the paragraphs describe a reading may be improved due to human activity problems, but provides no technical details on how the reading is actually adjusted. In particular the “adjusting, in real time, the weight readings” is not claimed as an additional element, no technical details are claimed that actually implement a technical solution, not even the neural network is claimed to be performing the adjusting. Applicant argues that the adjustment provides the improvement but provides no technical details on how the sensors reading is actually adjusted, instead the claimed adjustment is a high-level black box organization of data that is not an additional element capable of providing a technical solution to a technical problem recited in Applicant’s specification.
With respect to example 39, the claim recites an abstract idea of dietary tracking without providing a technical improvement in the performance of a neural network, the claim uses high-level well-known training of traditional neural networks to perform an abstract idea of dietary tracking, claim 39 does not perform an abstract idea of dietary tracking and is not similar, claim 39 is directed solely at training of a neural network model an does not recite an abstract idea, as such the argument is not persuasive.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.L./Examiner, Art Unit 3684
/RAJESH KHATTAR/Primary Examiner, Art Unit 3684
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